Instructions to use hipinis/20260718 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hipinis/20260718 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf hipinis/20260718:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf hipinis/20260718:Q8_0
Use Docker
docker model run hf.co/hipinis/20260718:Q8_0
- LM Studio
- Jan
- Ollama
How to use hipinis/20260718 with Ollama:
ollama run hf.co/hipinis/20260718:Q8_0
- Unsloth Studio
How to use hipinis/20260718 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for hipinis/20260718 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for hipinis/20260718 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hipinis/20260718 to start chatting
- Pi
How to use hipinis/20260718 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hipinis/20260718:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use hipinis/20260718 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "hipinis/20260718:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use hipinis/20260718 with Docker Model Runner:
docker model run hf.co/hipinis/20260718:Q8_0
- Lemonade
How to use hipinis/20260718 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hipinis/20260718:Q8_0
Run and chat with the model
lemonade run user.20260718-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use hipinis/20260718 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default hipinis/20260718:Q8_0
Run Hermes
hermes
- Atomic Chat
| from ..core import BOOLEAN, STRING, CATEGORY, any, logger | |
| class CSwitchFromAny: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "any": (any, ), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = (any, any,) | |
| RETURN_NAMES = ("on_true", "on_false",) | |
| FUNCTION = "execute" | |
| def execute(self, any,boolean=True): | |
| logger.debug("Any switch: " + str(boolean)) | |
| if boolean: | |
| return any, None | |
| else: | |
| return None, any | |
| class CSwitchBooleanAny: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": (any, {"lazy": True}), | |
| "on_false": (any, {"lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = (any,) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("Any switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |
| class CSwitchBooleanString: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": ("STRING", {"default": "", "lazy": True}), | |
| "on_false": ("STRING", {"default": "", "lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = ("STRING",) | |
| RETURN_NAMES = ("string",) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("String switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |
| class CSwitchBooleanConditioning: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": ("CONDITIONING", {"lazy": True}), | |
| "on_false": ("CONDITIONING", {"lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = ("CONDITIONING",) | |
| RETURN_NAMES = ("conditioning",) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("Conditioning switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |
| class CSwitchBooleanImage: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": ("IMAGE", {"lazy": True}), | |
| "on_false": ("IMAGE", {"lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = ("IMAGE",) | |
| RETURN_NAMES = ("image",) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("Image switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |
| class CSwitchBooleanLatent: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": ("LATENT", {"lazy": True}), | |
| "on_false": ("LATENT", {"lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = ("LATENT",) | |
| RETURN_NAMES = ("latent",) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("Latent switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |
| class CSwitchBooleanMask: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "on_true": ("MASK", {"lazy": True}), | |
| "on_false": ("MASK", {"lazy": True}), | |
| "boolean": BOOLEAN, | |
| } | |
| } | |
| CATEGORY = CATEGORY.MAIN.value + CATEGORY.SWITCH.value | |
| RETURN_TYPES = ("MASK",) | |
| RETURN_NAMES = ("mask",) | |
| FUNCTION = "execute" | |
| def check_lazy_status(self, on_true=None, on_false=None, boolean=True): | |
| needed = "on_true" if boolean else "on_false" | |
| return [needed] | |
| def execute(self, on_true, on_false, boolean=True): | |
| logger.debug("Mask switch: " + str(boolean)) | |
| if boolean: | |
| return (on_true,) | |
| else: | |
| return (on_false,) | |